SQLite

** value of 1 indicates that the proposed range scan is expected to visit
** approximately 1/100th (1%) of the rows selected by the nEq equality
** constraints (if any). A return value of 100 indicates that it is expected
** that the range scan will visit every row (100%) selected by the equality
** constraints.
**
** In the absence of sqlite_stat2 ANALYZE data, each range inequality
** reduces the search space by 3/4ths.  Hence a single constraint (x>?)
** results in a return of 25 and a range constraint (x>? AND x<?) results
** in a return of 6.
*/
static int whereRangeScanEst(
  Parse *pParse,       /* Parsing & code generating context */
  Index *p,            /* The index containing the range-compared column; "x" */
  int nEq,             /* index into p->aCol[] of the range-compared column */
  WhereTerm *pLower,   /* Lower bound on the range. ex: "x>123" Might be NULL */
  WhereTerm *pUpper,   /* Upper bound on the range. ex: "x<455" Might be NULL */

#else
  UNUSED_PARAMETER(pParse);
  UNUSED_PARAMETER(p);
  UNUSED_PARAMETER(nEq);
#endif
  assert( pLower || pUpper );
  *piEst = 100;
  if( pLower && (pLower->wtFlags & TERM_VNULL)==0 ) *piEst /= 4;
  if( pUpper ) *piEst /= 4;
  return rc;
}

#ifdef SQLITE_ENABLE_STAT2
/*
** Estimate the number of rows that will be returned based on
** an equality constraint x=VALUE and where that VALUE occurs in

  sqlite3ValueFree(pVal);
  return rc;
}
#endif /* defined(SQLITE_ENABLE_STAT2) */


/*
** Find the best query plan for accessing a particular table.  Write the
** best query plan and its cost into the WhereCost object supplied as the
** last parameter.
**
** The lowest cost plan wins.  The cost is an estimate of the amount of
** CPU and disk I/O needed to process the requested result.
** Factors that influence cost include:
**
**    *  The estimated number of rows that will be retrieved.  (The
**       fewer the better.)
**
**    *  Whether or not sorting must occur.
**
**    *  Whether or not there must be separate lookups in the
**       index and in the main table.
**
** If there was an INDEXED BY clause (pSrc->pIndex) attached to the table in
** the SQL statement, then this function only considers plans using the 
** named index. If no such plan is found, then the returned cost is
** SQLITE_BIG_DBL. If a plan is found that uses the named index, 
** then the cost is calculated in the usual way.
**
** If a NOT INDEXED clause (pSrc->notIndexed!=0) was attached to the table 
** in the SELECT statement, then no indexes are considered. However, the 
** selected plan may still take advantage of the built-in rowid primary key
** index.
*/
static void bestBtreeIndex(
  Parse *pParse,              /* The parsing context */
  WhereClause *pWC,           /* The WHERE clause */
  struct SrcList_item *pSrc,  /* The FROM clause term to search */
  Bitmask notReady,           /* Mask of cursors not available for indexing */

  if( pSrc->pIndex ){
    /* An INDEXED BY clause specifies a particular index to use */
    pIdx = pProbe = pSrc->pIndex;
    wsFlagMask = ~(WHERE_ROWID_EQ|WHERE_ROWID_RANGE);
    eqTermMask = idxEqTermMask;
  }else{
    /* There is no INDEXED BY clause.  Create a fake Index object in local
    ** variable sPk to represent the rowid primary key index.  Make this
    ** fake index the first in a chain of Index objects with all of the real
    ** indices to follow */
    Index *pFirst;                  /* First of real indices on the table */
    memset(&sPk, 0, sizeof(Index));
    sPk.nColumn = 1;
    sPk.aiColumn = &aiColumnPk;
    sPk.aiRowEst = aiRowEstPk;
    sPk.onError = OE_Replace;
    sPk.pTable = pSrc->pTab;
    aiRowEstPk[0] = pSrc->pTab->nRowEst;
    aiRowEstPk[1] = 1;
    pFirst = pSrc->pTab->pIndex;
    if( pSrc->notIndexed==0 ){
      /* The real indices of the table are only considered if the
      ** NOT INDEXED qualifier is omitted from the FROM clause */
      sPk.pNext = pFirst;
    }
    pProbe = &sPk;
    wsFlagMask = ~(
        WHERE_COLUMN_IN|WHERE_COLUMN_EQ|WHERE_COLUMN_NULL|WHERE_COLUMN_RANGE
    );
    eqTermMask = WO_EQ|WO_IN;
    pIdx = 0;
  }

  /* Loop over all indices looking for the best one to use
  */
  for(; pProbe; pIdx=pProbe=pProbe->pNext){
    const unsigned int * const aiRowEst = pProbe->aiRowEst;
    double cost;                /* Cost of using pProbe */
    double nRow;                /* Estimated number of rows in result set */
    int rev;                    /* True to scan in reverse order */
    double nSearch;             /* Estimated number of binary searches */
    int wsFlags = 0;
    Bitmask used = 0;

    /* The following variables are populated based on the properties of
    ** index being evaluated. They are then used to determine the expected
    ** cost and number of rows returned.
    **
    **  nEq: 
    **    Number of equality terms that can be implemented using the index.
    **    In other words, the number of initial fields in the index that
    **    are used in == or IN or NOT NULL constraints of the WHERE clause.
    **
    **  nInMul:  
    **    The "in-multiplier". This is an estimate of how many seek operations 
    **    SQLite must perform on the index in question. For example, if the 
    **    WHERE clause is:
    **
    **      WHERE a IN (1, 2, 3) AND b IN (4, 5, 6)

    **
    **    If there exists a WHERE term of the form "x IN (SELECT ...)", then 
    **    the sub-select is assumed to return 25 rows for the purposes of 
    **    determining nInMul.
    **
    **  bInEst:  
    **    Set to true if there was at least one "x IN (SELECT ...)" term used 
    **    in determining the value of nInMul.  Note that the RHS of the
    **    IN operator must be a SELECT, not a value list, for this variable
    **    to be true.
    **
    **  estBound:
    **    An estimate on the amount of the table that must be searched.  A
    **    value of 100 means the entire table is searched.  Range constraints
    **    might reduce this to a value less than 100 to indicate that only
    **    a fraction of the table needs searching.  In the absence of
    **    sqlite_stat2 ANALYZE data, a single inequality reduces the search
    **    space to 1/4rd its original size.  So an x>? constraint reduces
    **    estBound to 25.  Two constraints (x>? AND x<?) reduce estBound to 6.
    **
    **  bSort:   
    **    Boolean. True if there is an ORDER BY clause that will require an 
    **    external sort (i.e. scanning the index being evaluated will not 
    **    correctly order records).
    **
    **  bLookup: 
    **    Boolean. True if a table lookup is required for each index entry
    **    visited.  In other words, true if this is not a covering index.
    **    This is always false for the rowid primary key index of a table.
    **    For other indexes, it is true unless all the columns of the table
    **    used by the SELECT statement are present in the index (such an
    **    index is sometimes described as a covering index).
    **    For example, given the index on (a, b), the second of the following 
    **    two queries requires table b-tree lookups in order to find the value
    **    of column c, but the first does not because columns a and b are
    **    both available in the index.
    **
    **             SELECT a, b    FROM tbl WHERE a = 1;
    **             SELECT a, b, c FROM tbl WHERE a = 1;
    */
    int nEq;                      /* Number of == or IN terms matching index */
    int bInEst = 0;               /* True if "x IN (SELECT...)" seen */
    int nInMul = 1;               /* Number of distinct equalities to lookup */
    int estBound = 100;           /* Estimated reduction in search space */
    int nBound = 0;               /* Number of range constraints seen */
    int bSort = 0;                /* True if external sort required */
    int bLookup = 0;              /* True if not a covering index */
    WhereTerm *pTerm;             /* A single term of the WHERE clause */
#ifdef SQLITE_ENABLE_STAT2
    WhereTerm *pFirstTerm = 0;    /* First term matching the index */
#endif

    /* Determine the values of nEq and nInMul */
    for(nEq=0; nEq<pProbe->nColumn; nEq++){
      int j = pProbe->aiColumn[nEq];
      pTerm = findTerm(pWC, iCur, j, notReady, eqTermMask, pIdx);
      if( pTerm==0 ) break;
      wsFlags |= (WHERE_COLUMN_EQ|WHERE_ROWID_EQ);
      if( pTerm->eOperator & WO_IN ){
        Expr *pExpr = pTerm->pExpr;
        wsFlags |= WHERE_COLUMN_IN;
        if( ExprHasProperty(pExpr, EP_xIsSelect) ){
          /* "x IN (SELECT ...)":  Assume the SELECT returns 25 rows */
          nInMul *= 25;
          bInEst = 1;
        }else if( ALWAYS(pExpr->x.pList && pExpr->x.pList->nExpr) ){
          /* "x IN (value, value, ...)" */
          nInMul *= pExpr->x.pList->nExpr;
        }
      }else if( pTerm->eOperator & WO_ISNULL ){
        wsFlags |= WHERE_COLUMN_NULL;
      }
#ifdef SQLITE_ENABLE_STAT2
      if( nEq==0 && pProbe->aSample ) pFirstTerm = pTerm;
#endif

    }
#endif /* SQLITE_ENABLE_STAT2 */

    /* Adjust the number of rows and the cost downward to reflect rows
    ** that are excluded by range constraints.
    */
    nRow = (nRow * (double)estBound) / (double)100;
    if( nRow<1 ) nRow = 1;

    /* Assume constant cost to advance from one row to the next and
    ** logarithmic cost to do a binary search.  Hence, the initial cost
    ** is the number of output rows plus log2(table-size) times the
    ** number of binary searches.
    **
    ** Because fan-out on tables is so much higher than the fan-out on
    ** indices (because table btrees contain only integer keys in non-leaf
    ** nodes) we weight the cost of a table binary search as 1/10th the
    ** cost of an index binary search.
    */
    if( pIdx ){
      if( bLookup ){
        /* For an index lookup followed by a table lookup:
        **    nInMul index searches to find the start of each index range
        **  + nRow steps through the index
        **  + nRow table searches to lookup the table entry using the rowid
        */
        nSearch = nInMul + nRow/10;
      }else{
        /* For a covering index:
        **     nInMul binary searches to find the initial entry 
        **   + nRow steps through the index
        */
        nSearch = nInMul;
      }
    }else{
      /* For a rowid primary key lookup:
      **    nInMult binary searches to find the initial entry scaled by 1/10th
      **  + nRow steps through the table
      */
      nSearch = nInMul/10;
    }
    cost = nRow + nSearch*estLog(aiRowEst[0]);

    /* Add in the estimated cost of sorting the result.  This cost is expanded
    ** by a fudge factor of 3.0 to account for the fact that a sorting step 
    ** involves a write and is thus more expensive than a lookup step.
    */
    if( bSort ){
      cost += nRow*estLog(nRow)*(double)3;

        }else if( pTerm->eOperator & (WO_LT|WO_LE|WO_GT|WO_GE) ){
          if( nSkipRange ){
            /* Ignore the first nSkipRange range constraints since the index
            ** has already accounted for these */
            nSkipRange--;
          }else{
            /* Assume each additional range constraint reduces the result
            ** set size by a factor of 3.  Indexed range constraints reduce
            ** the search space by a larger factor: 4.  We make indexed range
            ** more selective intentionally because of the subjective 
            ** observation that indexed range constraints really are more
            ** selective in practice, on average. */
            nRow /= 3;
          }
        }else if( pTerm->eOperator!=WO_NOOP ){
          /* Any other expression lowers the output row count by half */
          nRow /= 2;
        }
      }

    execsql { INSERT INTO t3 VALUES($str, $str) }
  }
  execsql COMMIT
  execsql ANALYZE
} {}
do_test analyze2-4.2 {
  execsql { 
    PRAGMA automatic_index=OFF;
    SELECT tbl,idx,group_concat(sample,' ') 
    FROM sqlite_stat2 
    WHERE idx = 't3a' 
    GROUP BY tbl,idx;
    PRAGMA automatic_index=ON;
  }
} {t3 t3a {AfA bEj CEj dEj EEj fEj GEj hEj IEj jEj}}
do_test analyze2-4.3 {
  execsql { 
    SELECT tbl,idx,group_concat(sample,' ') 
    FROM sqlite_stat2 
    WHERE idx = 't3b' 

    DELETE FROM sqlite_stat2;
  }
  sqlite3 db test.db
  eqp { SELECT * FROM t5,t6 WHERE t5.rowid=t6.rowid AND 
        t5.a>1 AND t5.a<15 AND
        t6.a>1
  }
} {0 0 0 {SEARCH TABLE t5 USING COVERING INDEX t5i (a>? AND a<?) (~60000 rows)} 0 1 1 {SEARCH TABLE t6 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}}
do_test analyze2-6.2.2 {
  db cache flush
  execsql ANALYZE
  eqp { SELECT * FROM t5,t6 WHERE t5.rowid=t6.rowid AND 
        t5.a>1 AND t5.a<15 AND
        t6.a>1
  }

    DELETE FROM sqlite_master WHERE tbl_name = 'sqlite_stat1';
  }
  sqlite3 db test.db
  eqp { SELECT * FROM t5,t6 WHERE t5.rowid=t6.rowid AND 
        t5.a>1 AND t5.a<15 AND
        t6.a>1
  }
} {0 0 0 {SEARCH TABLE t5 USING COVERING INDEX t5i (a>? AND a<?) (~60000 rows)} 0 1 1 {SEARCH TABLE t6 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}}
do_test analyze2-6.2.5 {
  execsql { 
    PRAGMA writable_schema = 1;
    DELETE FROM sqlite_master WHERE tbl_name = 'sqlite_stat2';
  }
  sqlite3 db test.db
  eqp { SELECT * FROM t5,t6 WHERE t5.rowid=t6.rowid AND 
        t5.a>1 AND t5.a<15 AND
        t6.a>1
  }
} {0 0 0 {SEARCH TABLE t5 USING COVERING INDEX t5i (a>? AND a<?) (~60000 rows)} 0 1 1 {SEARCH TABLE t6 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}}
do_test analyze2-6.2.6 {
  execsql { 
    PRAGMA writable_schema = 1;
    INSERT INTO sqlite_master SELECT * FROM master;
  }
  sqlite3 db test.db
  execsql ANALYZE

  do_test analyze2-7.10 {
    incr_schema_cookie test.db
    execsql { SELECT * FROM sqlite_master } db1
    eqp { SELECT * FROM t5,t6 WHERE t5.rowid=t6.rowid AND 
          t5.a>1 AND t5.a<15 AND
          t6.a>1
    } db1
  } {0 0 0 {SEARCH TABLE t5 USING COVERING INDEX t5i (a>? AND a<?) (~1 rows)} 0 1 1 {SEARCH TABLE t6 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}}

  db1 close
  db2 close
  sqlite3_enable_shared_cache $::enable_shared_cache
}

finish_test

    append t [lindex {a b c d e f g h i j} [expr ($i%10)]]
    execsql { INSERT INTO t1 VALUES($i, $t) }
  }
  execsql COMMIT
} {}
do_eqp_test analyze3-2.2 {
  SELECT count(a) FROM t1 WHERE b LIKE 'a%'
} {0 0 0 {SEARCH TABLE t1 USING INDEX i1 (b>? AND b<?) (~30000 rows)}}
do_eqp_test analyze3-2.3 {
  SELECT count(a) FROM t1 WHERE b LIKE '%a'
} {0 0 0 {SCAN TABLE t1 (~500000 rows)}}

do_test analyze3-2.4 {
  sf_execsql { SELECT count(*) FROM t1 WHERE b LIKE 'a%' }
} {101 0 100}

  1    "EXPLAIN QUERY PLAN SELECT * FROM t1 WHERE b = 5" 
       {0 0 0 {SEARCH TABLE t1 USING INDEX sqlite_autoindex_t1_1 (b=?) (~1 rows)}}

  2    "EXPLAIN QUERY PLAN SELECT * FROM t2 ORDER BY b, c"
       {0 0 0 {SCAN TABLE t2 USING INDEX sqlite_autoindex_t2_1 (~1000000 rows)}}

  3    "EXPLAIN QUERY PLAN SELECT * FROM t2 WHERE b=10 AND c>10"
       {0 0 0 {SEARCH TABLE t2 USING INDEX sqlite_autoindex_t2_1 (b=? AND c>?) (~2 rows)}}
}

# EVIDENCE-OF: R-45493-35653 A CHECK constraint may be attached to a
# column definition or specified as a table constraint. In practice it
# makes no difference.
#
#   All the tests that deal with CHECK constraints below (4.11.* and 

# EVIDENCE-OF: R-22253-05302 sqlite> EXPLAIN QUERY PLAN SELECT t1.*,
# t2.* FROM t1, t2 WHERE t1.a=1 AND t1.b>2; 0|0|0|SEARCH TABLE t1
# USING COVERING INDEX i2 (a=? AND b>?) (~3 rows) 0|1|1|SCAN TABLE t2
# (~1000000 rows)
do_execsql_test 5.4.0 {CREATE TABLE t2(c, d)}
det 5.4.1 "SELECT t1.*, t2.* FROM t1, t2 WHERE t1.a=1 AND t1.b>2" {
  0 0 0 {SEARCH TABLE t1 USING COVERING INDEX i2 (a=? AND b>?) (~2 rows)}
  0 1 1 {SCAN TABLE t2 (~1000000 rows)}
}

# EVIDENCE-OF: R-21040-07025 sqlite> EXPLAIN QUERY PLAN SELECT t1.*,
# t2.* FROM t2, t1 WHERE t1.a=1 AND t1.b>2; 0|0|1|SEARCH TABLE t1
# USING COVERING INDEX i2 (a=? AND b>?) (~3 rows) 0|1|0|SCAN TABLE t2
# (~1000000 rows)
det 5.5 "SELECT t1.*, t2.* FROM t2, t1 WHERE t1.a=1 AND t1.b>2" {
  0 0 1 {SEARCH TABLE t1 USING COVERING INDEX i2 (a=? AND b>?) (~2 rows)}
  0 1 0 {SCAN TABLE t2 (~1000000 rows)}
}

# EVIDENCE-OF: R-39007-61103 sqlite> CREATE INDEX i3 ON t1(b);
# sqlite> EXPLAIN QUERY PLAN SELECT * FROM t1 WHERE a=1 OR b=2;
# 0|0|0|SEARCH TABLE t1 USING COVERING INDEX i2 (a=?) (~10 rows)
# 0|0|0|SEARCH TABLE t1 USING INDEX i3 (b=?) (~10 rows)

# Test embedding an INDEXED BY in a CREATE VIEW statement. This block
# also tests that nothing bad happens if an index refered to by
# a CREATE VIEW statement is dropped and recreated.
#
do_execsql_test indexedby-5.1 {
  CREATE VIEW v2 AS SELECT * FROM t1 INDEXED BY i1 WHERE a > 5;
  EXPLAIN QUERY PLAN SELECT * FROM v2 
} {0 0 0 {SEARCH TABLE t1 USING INDEX i1 (a>?) (~250000 rows)}}
do_execsql_test indexedby-5.2 {
  EXPLAIN QUERY PLAN SELECT * FROM v2 WHERE b = 10 
} {0 0 0 {SEARCH TABLE t1 USING INDEX i1 (a>?) (~25000 rows)}}
do_test indexedby-5.3 {
  execsql { DROP INDEX i1 }
  catchsql { SELECT * FROM v2 }
} {1 {no such index: i1}}
do_test indexedby-5.4 {
  # Recreate index i1 in such a way as it cannot be used by the view query.
  execsql { CREATE INDEX i1 ON t1(b) }

      INSERT INTO t10 VALUES(12,12,12,12,12,12);
      INSERT INTO t10 VALUES(123,123,123,123,123,123);
      INSERT INTO t10 VALUES(234,234,234,234,234,234);
      INSERT INTO t10 VALUES(345,345,345,345,345,345);
      INSERT INTO t10 VALUES(45,45,45,45,45,45);
    }
    count {
      SELECT a FROM t10 WHERE b LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.2 {
    count {
      SELECT a FROM t10 WHERE c LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.3 {
    count {
      SELECT a FROM t10 WHERE d LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.4 {
    count {
      SELECT a FROM t10 WHERE e LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.5 {
    count {
      SELECT a FROM t10 WHERE f LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 3 like 0}
  do_test like-10.6 {
    count {
      SELECT a FROM t10 WHERE a LIKE '12%' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.10 {
    execsql {
      CREATE TABLE t10b(
        a INTEGER PRIMARY KEY,
        b INTEGER UNIQUE,
        c NUMBER UNIQUE,
        d BLOB UNIQUE,
        e UNIQUE,
        f TEXT UNIQUE
      );
      INSERT INTO t10b SELECT * FROM t10;
    }
    count {
      SELECT a FROM t10b WHERE b GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.11 {
    count {
      SELECT a FROM t10b WHERE c GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.12 {
    count {
      SELECT a FROM t10b WHERE d GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.13 {
    count {
      SELECT a FROM t10b WHERE e GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
  do_test like-10.14 {
    count {
      SELECT a FROM t10b WHERE f GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 3 like 0}
  do_test like-10.15 {
    count {
      SELECT a FROM t10b WHERE a GLOB '12*' ORDER BY +a;
    }
  } {12 123 scan 5 like 6}
}

# LIKE and GLOB where the default collating sequence is not appropriate
# but an index with the appropriate collating sequence exists.
#

    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY a;
  }
} {abc abcd nosort t11 *}
do_test like-11.3 {
  queryplan {
    PRAGMA case_sensitive_like=OFF;
    CREATE INDEX t11b ON t11(b);
    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY +a;
  }
} {abc abcd ABC ABCD sort {} t11b}
do_test like-11.4 {
  queryplan {
    PRAGMA case_sensitive_like=ON;
    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY a;
  }
} {abc abcd nosort t11 *}
do_test like-11.5 {
  queryplan {
    PRAGMA case_sensitive_like=OFF;
    DROP INDEX t11b;
    CREATE INDEX t11bnc ON t11(b COLLATE nocase);
    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY +a;
  }
} {abc abcd ABC ABCD sort {} t11bnc}
do_test like-11.6 {
  queryplan {
    CREATE INDEX t11bb ON t11(b COLLATE binary);
    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY +a;
  }
} {abc abcd ABC ABCD sort {} t11bnc}
do_test like-11.7 {
  queryplan {
    PRAGMA case_sensitive_like=ON;
    SELECT b FROM t11 WHERE b LIKE 'abc%' ORDER BY +a;
  }
} {abc abcd sort {} t11bb}
do_test like-11.8 {
  queryplan {
    PRAGMA case_sensitive_like=OFF;
    SELECT b FROM t11 WHERE b GLOB 'abc*' ORDER BY +a;
  }
} {abc abcd sort {} t11bb}
do_test like-11.9 {
  queryplan {
    CREATE INDEX t11cnc ON t11(c COLLATE nocase);
    CREATE INDEX t11cb ON t11(c COLLATE binary);
    SELECT c FROM t11 WHERE c LIKE 'abc%' ORDER BY +a;
  }
} {abc abcd ABC ABCD sort {} t11cnc}
do_test like-11.10 {
  queryplan {
    SELECT c FROM t11 WHERE c GLOB 'abc*' ORDER BY +a;
  }
} {abc abcd sort {} t11cb}


finish_test

    INSERT INTO t1 VALUES('1', 'I',   'one');
    INSERT INTO t1 VALUES('2', 'IV',  'four');
    INSERT INTO t1 VALUES('2', NULL,  'three');
    INSERT INTO t1 VALUES('2', 'II',  'two');
    INSERT INTO t1 VALUES('2', 'V',   'five');
    INSERT INTO t1 VALUES('3', 'VI',  'six');
    COMMIT;
    PRAGMA automatic_index=OFF;
  }
} {}
do_test minmax3-1.1.1 {
  # Linear scan.
  count { SELECT max(y) FROM t1 WHERE x = '2'; }
} {V 5}
do_test minmax3-1.1.2 {

  CREATE TABLE t301(a INTEGER PRIMARY KEY,b,c);
  CREATE INDEX t301c ON t301(c);
  INSERT INTO t301 VALUES(1,2,3);
  CREATE TABLE t302(x, y);
  ANALYZE;
  explain query plan SELECT * FROM t302, t301 WHERE t302.x=5 AND t301.a=t302.y;
} {
  0 0 0 {SCAN TABLE t302 (~1 rows)} 
  0 1 1 {SEARCH TABLE t301 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}
}
exit
do_execsql_test where3-3.1 {
  explain query plan
  SELECT * FROM t301, t302 WHERE t302.x=5 AND t301.a=t302.y;
} {
  0 0 1 {SCAN TABLE t302 (~1 rows)} 
  0 1 0 {SEARCH TABLE t301 USING INTEGER PRIMARY KEY (rowid=?) (~1 rows)}
}

# Verify that when there are multiple tables in a join which must be
# full table scans that the query planner attempts put the table with
# the fewest number of output rows as the outer loop.
#

  # Likewise, inequalities in an AND are preferred over inequalities in
  # an OR.
  #
  do_execsql_test where9-5.3 {
    EXPLAIN QUERY PLAN SELECT a FROM t1 WHERE b>1000 AND (c>=31031 OR d IS NULL)
  } {
    0 0 0 {SEARCH TABLE t1 USING INDEX t1b (b>?) (~125000 rows)}
  }
}

############################################################################
# Make sure OR-clauses work correctly on UPDATE and DELETE statements.

do_test where9-6.2.1 {